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1. Amino acid and protein specificity of protein fatty acylation in Caenorhabditis elegans

   https://doi.org/10.1073/pnas.2307515121  

   Zhang, Bingsen; Yu, Yan; Fox, Bennett W; Liu, Yinong; Thirumalaikumar, Venkatesh P; Skirycz, Aleksandra; Lin, Hening; Schroeder, Frank C (January 2024, Proceedings of the National Academy of Sciences)

   Protein lipidation plays critical roles in regulating protein function and localization. However, the chemical diversity and specificity of fatty acyl group utilization have not been investigated using untargeted approaches, and it is unclear to what extent structures and biosynthetic origins ofS-acyl moieties differ fromN- andO-fatty acylation. Here, we show that fatty acylation patterns inCaenorhabditis elegansdiffer markedly between different amino acid residues. Hydroxylamine capture revealed predominant cysteineS-acylation with 15-methylhexadecanoic acid (isoC17:0), a monomethyl branched-chain fatty acid (mmBCFA) derived from endogenous leucine catabolism. In contrast, enzymatic protein hydrolysis showed that N-terminal glycine was acylated almost exclusively with straight-chain myristic acid, whereas lysine was acylated preferentially with two different mmBCFAs and serine was acylated promiscuously with a broad range of fatty acids, including eicosapentaenoic acid. Global profiling of fatty acylated proteins using a set of click chemistry–capable alkyne probes for branched- and straight-chain fatty acids uncovered 1,013S-acylated proteins and 510 hydroxylamine-resistantN- orO-acylated proteins. Subsets ofS-acylated proteins were labeled almost exclusively by either a branched-chain or a straight-chain probe, demonstrating acylation specificity at the protein level. Acylation specificity was confirmed for selected examples, including theS-acyltransferase DHHC-10. Last, homology searches for the identified acylated proteins revealed a high degree of conservation of acylation site patterns across metazoa. Our results show that protein fatty acylation patterns integrate distinct branches of lipid metabolism in a residue- and protein-specific manner, providing a basis for mechanistic studies at both the amino acid and protein levels. 

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2. Materials composed of the Drosophila melanogaster protein ultrabithorax are cytocompatible: Ubx Protein Materials are Cytocompatible

   https://doi.org/10.1002/jbm.a.34675  

   Patterson, Jan L.; Abbey, Colette A.; Bayless, Kayla J.; Bondos, Sarah E. (January 2014, Journal of Biomedical Materials Research Part A)
3. mebipred : identifying metal-binding potential in protein sequence

   https://doi.org/10.1093/bioinformatics/btac358  

   Aptekmann, A. A.; Buongiorno, J.; Giovannelli, D.; Glamoclija, M.; Ferreiro, D. U.; Bromberg, Y.; Cowen, ed., Lenore (May 2022, Bioinformatics)

   Abstract Motivationmetal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability. Resultswe developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred’s utility in analyzing microbiome metal requirements. Availability and implementationmebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/. Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. Controlling and quantifying protein concentration in Escherichia coli

   https://doi.org/10.1002/pro.3637  

   Speer, Shannon L.; Guseman, Alex J.; Patteson, Jon B.; Ehrmann, Brandie M.; Pielak, Gary J. (May 2019, Protein Science)

   Abstract The cellular environment is dynamic and complex, involving thousands of different macromolecules with total concentrations of hundreds of grams per liter. However, most biochemistry is conducted in dilute buffer where the concentration of macromolecules is less than 10 g/L. High concentrations of macromolecules affect protein stability, function, and protein complex formation, but to understand these phenomena fully we need to know the concentration of the test protein in cells. Here, we quantify the concentration of an overexpressed recombinant protein, a variant of the B1 domain of protein G, in Tuner (DE3)™Escherichia colicells as a function of inducer concentration. We find that the protein expression level is controllable, and expression saturates at over 2 mMupon induction with 0.4 mMisopropyl β‐d‐thiogalactoside. We discuss the results in terms of what can and cannot be learned from in‐cell protein NMR studies inE. coli. 

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5. Hyperstable De Novo Protein with a Dimeric Bisecting Topology

   https://doi.org/10.1021/acssynbio.9b00501  

   Kimura, Naoya; Mochizuki, Kenji; Umezawa, Koji; Hecht, Michael H.; Arai, Ryoichi (December 2019, ACS Synthetic Biology)

   null (Ed.)